1,680 research outputs found
Graph-Based Interaction-Aware Multimodal 2D Vehicle Trajectory Prediction using Diffusion Graph Convolutional Networks
Predicting vehicle trajectories is crucial for ensuring automated vehicle
operation efficiency and safety, particularly on congested multi-lane highways.
In such dynamic environments, a vehicle's motion is determined by its
historical behaviors as well as interactions with surrounding vehicles. These
intricate interactions arise from unpredictable motion patterns, leading to a
wide range of driving behaviors that warrant in-depth investigation. This study
presents the Graph-based Interaction-aware Multi-modal Trajectory Prediction
(GIMTP) framework, designed to probabilistically predict future vehicle
trajectories by effectively capturing these interactions. Within this
framework, vehicles' motions are conceptualized as nodes in a time-varying
graph, and the traffic interactions are represented by a dynamic adjacency
matrix. To holistically capture both spatial and temporal dependencies embedded
in this dynamic adjacency matrix, the methodology incorporates the Diffusion
Graph Convolutional Network (DGCN), thereby providing a graph embedding of both
historical states and future states. Furthermore, we employ a driving
intention-specific feature fusion, enabling the adaptive integration of
historical and future embeddings for enhanced intention recognition and
trajectory prediction. This model gives two-dimensional predictions for each
mode of longitudinal and lateral driving behaviors and offers probabilistic
future paths with corresponding probabilities, addressing the challenges of
complex vehicle interactions and multi-modality of driving behaviors.
Validation using real-world trajectory datasets demonstrates the efficiency and
potential
Towards trustworthy multi-modal motion prediction: Holistic evaluation and interpretability of outputs
Predicting the motion of other road agents enables autonomous vehicles to
perform safe and efficient path planning. This task is very complex, as the
behaviour of road agents depends on many factors and the number of possible
future trajectories can be considerable (multi-modal). Most prior approaches
proposed to address multi-modal motion prediction are based on complex machine
learning systems that have limited interpretability. Moreover, the metrics used
in current benchmarks do not evaluate all aspects of the problem, such as the
diversity and admissibility of the output. In this work, we aim to advance
towards the design of trustworthy motion prediction systems, based on some of
the requirements for the design of Trustworthy Artificial Intelligence. We
focus on evaluation criteria, robustness, and interpretability of outputs.
First, we comprehensively analyse the evaluation metrics, identify the main
gaps of current benchmarks, and propose a new holistic evaluation framework. We
then introduce a method for the assessment of spatial and temporal robustness
by simulating noise in the perception system. To enhance the interpretability
of the outputs and generate more balanced results in the proposed evaluation
framework, we propose an intent prediction layer that can be attached to
multi-modal motion prediction models. The effectiveness of this approach is
assessed through a survey that explores different elements in the visualization
of the multi-modal trajectories and intentions. The proposed approach and
findings make a significant contribution to the development of trustworthy
motion prediction systems for autonomous vehicles, advancing the field towards
greater safety and reliability.Comment: 16 pages, 7 figures, 6 table
EqDrive: Efficient Equivariant Motion Forecasting with Multi-Modality for Autonomous Driving
Forecasting vehicular motions in autonomous driving requires a deep
understanding of agent interactions and the preservation of motion equivariance
under Euclidean geometric transformations. Traditional models often lack the
sophistication needed to handle the intricate dynamics inherent to autonomous
vehicles and the interaction relationships among agents in the scene. As a
result, these models have a lower model capacity, which then leads to higher
prediction errors and lower training efficiency. In our research, we employ
EqMotion, a leading equivariant particle, and human prediction model that also
accounts for invariant agent interactions, for the task of multi-agent vehicle
motion forecasting. In addition, we use a multi-modal prediction mechanism to
account for multiple possible future paths in a probabilistic manner. By
leveraging EqMotion, our model achieves state-of-the-art (SOTA) performance
with fewer parameters (1.2 million) and a significantly reduced training time
(less than 2 hours).Comment: 6 pages, 7 figure
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs
Enabling resilient autonomous motion planning requires robust predictions of
surrounding road users' future behavior. In response to this need and the
associated challenges, we introduce our model titled MTP-GO. The model encodes
the scene using temporal graph neural networks to produce the inputs to an
underlying motion model. The motion model is implemented using neural ordinary
differential equations where the state-transition functions are learned with
the rest of the model. Multimodal probabilistic predictions are obtained by
combining the concept of mixture density networks and Kalman filtering. The
results illustrate the predictive capabilities of the proposed model across
various data sets, outperforming several state-of-the-art methods on a number
of metrics.Comment: Code: https://github.com/westny/mtp-g
- …